





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			['image_processor', 'tokenizer']
     __magic_name__   =			'BlipImageProcessor'
     __magic_name__   =			'AutoTokenizer'




     def __init__(    self				,     snake_case_				,     snake_case_				,     snake_case_							):
         super().__init__(snake_case_				,     snake_case_							)

         # add QFormer tokenizer
         _A     =							qformer_tokenizer




     def __call__(    self				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = True				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = 0				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = False				,     snake_case_ = True				,     snake_case_ = None				,     **snake_case_				,     ):
         if images is None and text is None:
             raise ValueError('You have to specify at least images or text.'							)

         _A     =							BatchFeature()

         if text is not None:
             _A     =							self.tokenizer(
                 text=snake_case_				,     add_special_tokens=snake_case_				,     padding=snake_case_				,     truncation=snake_case_				,     max_length=snake_case_				,     stride=snake_case_				,     pad_to_multiple_of=snake_case_				,     return_attention_mask=snake_case_				,     return_overflowing_tokens=snake_case_				,     return_special_tokens_mask=snake_case_				,     return_offsets_mapping=snake_case_				,     return_token_type_ids=snake_case_				,     return_length=snake_case_				,     verbose=snake_case_				,     return_tensors=snake_case_				,     **snake_case_				,     )
             encoding.update(snake_case_							)
             _A     =							self.qformer_tokenizer(
                 text=snake_case_				,     add_special_tokens=snake_case_				,     padding=snake_case_				,     truncation=snake_case_				,     max_length=snake_case_				,     stride=snake_case_				,     pad_to_multiple_of=snake_case_				,     return_attention_mask=snake_case_				,     return_overflowing_tokens=snake_case_				,     return_special_tokens_mask=snake_case_				,     return_offsets_mapping=snake_case_				,     return_token_type_ids=snake_case_				,     return_length=snake_case_				,     verbose=snake_case_				,     return_tensors=snake_case_				,     **snake_case_				,     )
             _A     =							qformer_text_encoding.pop('input_ids'							)
             _A     =							qformer_text_encoding.pop('attention_mask'							)

         if images is not None:
             _A     =							self.image_processor(snake_case_				,     return_tensors=snake_case_							)
             encoding.update(snake_case_							)

         return encoding




     def        lowerCAmelCase__		(    self				,     *snake_case_				,     **snake_case_							):
         return self.tokenizer.batch_decode(*snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     *snake_case_				,     **snake_case_							):
         return self.tokenizer.decode(*snake_case_				,     **snake_case_							)




     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def        lowerCAmelCase__		(    self							):
         _A     =							self.tokenizer.model_input_names
         _A     =							self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     **snake_case_							):
         if os.path.isfile(snake_case_							):
             raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"							)
         os.makedirs(snake_case_				,     exist_ok=snake_case_							)
         _A     =							os.path.join(snake_case_				,     'qformer_tokenizer'							)
         self.qformer_tokenizer.save_pretrained(snake_case_							)
         return super().save_pretrained(snake_case_				,     **snake_case_							)




     @classmethod
     def        lowerCAmelCase__		(    cls				,     snake_case_				,     **snake_case_							):
         _A     =							AutoTokenizer.from_pretrained(snake_case_				,     subfolder='qformer_tokenizer'							)
         _A     =							cls._get_arguments_from_pretrained(snake_case_				,     **snake_case_							)
         args.append(snake_case_							)
         return cls(*snake_case_							)










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Optional[int]					=		logging.get_logger(__name__)

lowerCAmelCase:   int					=		{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """ctrl"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__( self					:    Optional[int]		,     __snake_case					:    Union[str, Any]=24_65_34		,     __snake_case					:    Dict=2_56		,     __snake_case					:    Optional[int]=12_80		,     __snake_case					:    Optional[int]=81_92		,     __snake_case					:    Union[str, Any]=48		,     __snake_case					:    str=16		,     __snake_case					:    Any=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    str=1e-6		,     __snake_case					:    List[str]=0.02		,     __snake_case					:    int=True		,     **__snake_case					:    Any		,     ):
      a		:   int	  =	vocab_size
      a		:   Union[str, Any]	  =	n_positions
      a		:   Optional[int]	  =	n_embd
      a		:   Dict	  =	n_layer
      a		:   Union[str, Any]	  =	n_head
      a		:   Tuple	  =	dff
      a		:   Union[str, Any]	  =	resid_pdrop
      a		:   Tuple	  =	embd_pdrop
      a		:   int	  =	layer_norm_epsilon
      a		:   str	  =	initializer_range

      a		:   Any	  =	use_cache

      super().__init__(**__snake_case      )